An Object-Oriented CNN Model Based on Improved Superpixel Segmentation for High-Resolution Remote Sensing Image Classification

Object-oriented convolutional neural network (CNN) has been proven to be an effective classification method for very fine spatial resolution remotely sensed imagery. It can obtain higher accuracy and well edge preservation results due to the combination of advantages of image segmentation and deep network at the same time. However, the mismatch with the real boundary of the ground object is still a problem that needs to be solved further. In addition, a specific CNN model that can learn better feature representations also plays an important role in improving classification accuracy. For these purposes, we proposed an improved sample linear iterative cluster (SLIC) to obtain better segmentation edges. This algorithm overcomes the limitation of the input feature dimension in SLIC and improves the boundary performance by using more features. Besides, in order to obtain better feature representations, a new CNN model has also been developed, which can make full use of spectral information to learn first-order and second-order fusion features for classification. This method has been verified on four real remote sensing images. Compared with other methods, the proposed method achieves better performance in terms of edge and classification accuracy.